{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "housing_df = pd.read_csv('HousingData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.191178</td>\n",
       "      <td>0.401863</td>\n",
       "      <td>-0.054355</td>\n",
       "      <td>0.417130</td>\n",
       "      <td>-0.219150</td>\n",
       "      <td>0.354342</td>\n",
       "      <td>-0.374166</td>\n",
       "      <td>0.624765</td>\n",
       "      <td>0.580595</td>\n",
       "      <td>0.281110</td>\n",
       "      <td>-0.381411</td>\n",
       "      <td>0.444943</td>\n",
       "      <td>-0.391363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>-0.191178</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.531871</td>\n",
       "      <td>-0.037229</td>\n",
       "      <td>-0.513704</td>\n",
       "      <td>0.320800</td>\n",
       "      <td>-0.563801</td>\n",
       "      <td>0.656739</td>\n",
       "      <td>-0.310919</td>\n",
       "      <td>-0.312371</td>\n",
       "      <td>-0.414046</td>\n",
       "      <td>0.171303</td>\n",
       "      <td>-0.414193</td>\n",
       "      <td>0.373136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.401863</td>\n",
       "      <td>-0.531871</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059859</td>\n",
       "      <td>0.764866</td>\n",
       "      <td>-0.390234</td>\n",
       "      <td>0.638431</td>\n",
       "      <td>-0.711709</td>\n",
       "      <td>0.604533</td>\n",
       "      <td>0.731055</td>\n",
       "      <td>0.390954</td>\n",
       "      <td>-0.360532</td>\n",
       "      <td>0.590690</td>\n",
       "      <td>-0.481772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>-0.054355</td>\n",
       "      <td>-0.037229</td>\n",
       "      <td>0.059859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.075097</td>\n",
       "      <td>0.104885</td>\n",
       "      <td>0.078831</td>\n",
       "      <td>-0.093971</td>\n",
       "      <td>0.001468</td>\n",
       "      <td>-0.032304</td>\n",
       "      <td>-0.111304</td>\n",
       "      <td>0.051264</td>\n",
       "      <td>-0.047424</td>\n",
       "      <td>0.181391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.417130</td>\n",
       "      <td>-0.513704</td>\n",
       "      <td>0.764866</td>\n",
       "      <td>0.075097</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>0.731548</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.582641</td>\n",
       "      <td>-0.427321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>-0.219150</td>\n",
       "      <td>0.320800</td>\n",
       "      <td>-0.390234</td>\n",
       "      <td>0.104885</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.247337</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.614339</td>\n",
       "      <td>0.695360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.354342</td>\n",
       "      <td>-0.563801</td>\n",
       "      <td>0.638431</td>\n",
       "      <td>0.078831</td>\n",
       "      <td>0.731548</td>\n",
       "      <td>-0.247337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.744844</td>\n",
       "      <td>0.458349</td>\n",
       "      <td>0.509114</td>\n",
       "      <td>0.269226</td>\n",
       "      <td>-0.275303</td>\n",
       "      <td>0.602891</td>\n",
       "      <td>-0.394656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-0.374166</td>\n",
       "      <td>0.656739</td>\n",
       "      <td>-0.711709</td>\n",
       "      <td>-0.093971</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.744844</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.493328</td>\n",
       "      <td>0.249929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.624765</td>\n",
       "      <td>-0.310919</td>\n",
       "      <td>0.604533</td>\n",
       "      <td>0.001468</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>0.458349</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>0.479541</td>\n",
       "      <td>-0.381626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.580595</td>\n",
       "      <td>-0.312371</td>\n",
       "      <td>0.731055</td>\n",
       "      <td>-0.032304</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>0.509114</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>0.536110</td>\n",
       "      <td>-0.468536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.281110</td>\n",
       "      <td>-0.414046</td>\n",
       "      <td>0.390954</td>\n",
       "      <td>-0.111304</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.269226</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>0.375966</td>\n",
       "      <td>-0.507787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.381411</td>\n",
       "      <td>0.171303</td>\n",
       "      <td>-0.360532</td>\n",
       "      <td>0.051264</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.275303</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369889</td>\n",
       "      <td>0.333461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.444943</td>\n",
       "      <td>-0.414193</td>\n",
       "      <td>0.590690</td>\n",
       "      <td>-0.047424</td>\n",
       "      <td>0.582641</td>\n",
       "      <td>-0.614339</td>\n",
       "      <td>0.602891</td>\n",
       "      <td>-0.493328</td>\n",
       "      <td>0.479541</td>\n",
       "      <td>0.536110</td>\n",
       "      <td>0.375966</td>\n",
       "      <td>-0.369889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.735822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDV</th>\n",
       "      <td>-0.391363</td>\n",
       "      <td>0.373136</td>\n",
       "      <td>-0.481772</td>\n",
       "      <td>0.181391</td>\n",
       "      <td>-0.427321</td>\n",
       "      <td>0.695360</td>\n",
       "      <td>-0.394656</td>\n",
       "      <td>0.249929</td>\n",
       "      <td>-0.381626</td>\n",
       "      <td>-0.468536</td>\n",
       "      <td>-0.507787</td>\n",
       "      <td>0.333461</td>\n",
       "      <td>-0.735822</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "CRIM     1.000000 -0.191178  0.401863 -0.054355  0.417130 -0.219150  0.354342   \n",
       "ZN      -0.191178  1.000000 -0.531871 -0.037229 -0.513704  0.320800 -0.563801   \n",
       "INDUS    0.401863 -0.531871  1.000000  0.059859  0.764866 -0.390234  0.638431   \n",
       "CHAS    -0.054355 -0.037229  0.059859  1.000000  0.075097  0.104885  0.078831   \n",
       "NOX      0.417130 -0.513704  0.764866  0.075097  1.000000 -0.302188  0.731548   \n",
       "RM      -0.219150  0.320800 -0.390234  0.104885 -0.302188  1.000000 -0.247337   \n",
       "AGE      0.354342 -0.563801  0.638431  0.078831  0.731548 -0.247337  1.000000   \n",
       "DIS     -0.374166  0.656739 -0.711709 -0.093971 -0.769230  0.205246 -0.744844   \n",
       "RAD      0.624765 -0.310919  0.604533  0.001468  0.611441 -0.209847  0.458349   \n",
       "TAX      0.580595 -0.312371  0.731055 -0.032304  0.668023 -0.292048  0.509114   \n",
       "PTRATIO  0.281110 -0.414046  0.390954 -0.111304  0.188933 -0.355501  0.269226   \n",
       "B       -0.381411  0.171303 -0.360532  0.051264 -0.380051  0.128069 -0.275303   \n",
       "LSTAT    0.444943 -0.414193  0.590690 -0.047424  0.582641 -0.614339  0.602891   \n",
       "MEDV    -0.391363  0.373136 -0.481772  0.181391 -0.427321  0.695360 -0.394656   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO         B     LSTAT      MEDV  \n",
       "CRIM    -0.374166  0.624765  0.580595  0.281110 -0.381411  0.444943 -0.391363  \n",
       "ZN       0.656739 -0.310919 -0.312371 -0.414046  0.171303 -0.414193  0.373136  \n",
       "INDUS   -0.711709  0.604533  0.731055  0.390954 -0.360532  0.590690 -0.481772  \n",
       "CHAS    -0.093971  0.001468 -0.032304 -0.111304  0.051264 -0.047424  0.181391  \n",
       "NOX     -0.769230  0.611441  0.668023  0.188933 -0.380051  0.582641 -0.427321  \n",
       "RM       0.205246 -0.209847 -0.292048 -0.355501  0.128069 -0.614339  0.695360  \n",
       "AGE     -0.744844  0.458349  0.509114  0.269226 -0.275303  0.602891 -0.394656  \n",
       "DIS      1.000000 -0.494588 -0.534432 -0.232471  0.291512 -0.493328  0.249929  \n",
       "RAD     -0.494588  1.000000  0.910228  0.464741 -0.444413  0.479541 -0.381626  \n",
       "TAX     -0.534432  0.910228  1.000000  0.460853 -0.441808  0.536110 -0.468536  \n",
       "PTRATIO -0.232471  0.464741  0.460853  1.000000 -0.177383  0.375966 -0.507787  \n",
       "B        0.291512 -0.444413 -0.441808 -0.177383  1.000000 -0.369889  0.333461  \n",
       "LSTAT   -0.493328  0.479541  0.536110  0.375966 -0.369889  1.000000 -0.735822  \n",
       "MEDV     0.249929 -0.381626 -0.468536 -0.507787  0.333461 -0.735822  1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "# Set up seaborn dark grid\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = housing_df.corr()\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(corr, xticklabels=corr.columns.values,\n",
    "yticklabels=corr.columns.values, cmap=\"Blues\", linewidths=1.25, alpha=0.8)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
